Search Results (4,039)

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that severely impacts motor function, necessitating early detection for effective management. However, current diagnostic methods are expensive and resource-intensive, limiting their accessibility. This study proposes a non-invasive, gait-based screening approach for PD using time-series analysis of video-derived motion data. Gait patterns indicative of PD are analyzed using videos containing walking sequences of PD subjects. The video data are processed via computer vision and human pose estimation techniques to extract key body points. Classification is performed using K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) networks in conjunction with time-series techniques, including Dynamic Time Warping (DTW), Bag of Patterns (BoP), and Symbolic Aggregate Approximation (SAX). KNN classifies based on similarity measures derived from these methods, while LSTM captures complex temporal dependencies. Additionally, Shapelet-based Classification is independently explored for its ability to serve as a self-contained classifier by extracting discriminative motion patterns. On a self-collected dataset (43 instances: 8 PD and 35 healthy), DTW-based classification achieved 88.89% accuracy for both KNN and LSTM. On an external dataset (294 instances: 150 healthy and 144 PD with varying severity), KNN and LSTM achieved 71.19% and 57.63% accuracy, respectively. The proposed approach enhances PD detection through a cost-effective, non-invasive methodology, supporting early diagnosis and disease monitoring. By integrating machine learning with clinical insights, this study demonstrates the potential of AI-driven solutions in advancing PD screening and management. Full article

While interleaved learning has been shown to enhance young adults’ acquisition of confusable natural categories, its effects on children’s natural category learning remain underexplored. The present study investigated the effects of study schedule (interleaving vs. blocking) on both categorization accuracy and the accuracy of metacognitive judgments during the learning of natural rock categories, comparing children and young adults. In Experiment 1, participants studied under blocked or interleaved conditions and subsequently provided global judgments of their learning. In Experiment 2, we employed a self-paced learning paradigm that required learners to regulate their own study time. Additionally, participants made item-by-item judgments of their learning during the study phase. Across both experiments, we found that interleaved learning significantly improved categorization accuracy, with young adults benefiting more than children. Regarding metacognitive monitoring, interleaving reduced overconfidence in children but led to underconfidence in young adults, as reflected in both global and item-level judgments. These findings suggest that the benefits of interleaved learning for category performance and metacognitive monitoring vary with age, highlighting age-related differences in the effectiveness of interleaved learning. Full article

Background/Objectives: Anosognosia for hemiplegia (AHP) is a multifaceted syndrome in which stroke survivors fail to recognize motor impairments. Although AHP has significant clinical implications, rehabilitation strategies have remained fragmented and underexplored. This systematic review aimed to critically evaluate rehabilitation interventions for AHP published between 2006 and 2025, categorize intervention types, and assess clinical outcomes to inform future research and practice. Methods: A structured search was conducted in the PubMed and PsycINFO databases on 31 March 2025, using predefined keywords related to stroke, anosognosia, and rehabilitation. The eligible studies included randomized controlled trials, case–control studies, and case studies. Following title, abstract, and full-text screening, nine studies focusing on rehabilitation interventions for AHP were selected and analyzed. Results: The interventions reviewed included sensorimotor recalibration techniques, neuromodulatory approaches, error-based cognitive training, and self-observation in video replay strategies. Interventions emphasizing motor intention monitoring, error correction, and self-observation were more consistently associated with durable improvements in motor awareness than neglect-based spatial interventions were. However, many studies were limited by small sample sizes and a lack of standardized outcome measures. Assessment methodologies vary widely, highlighting the need for multidimensional theory-driven evaluation tools. Conclusions: Effective rehabilitation for AHP requires strategies targeting disrupted self-monitoring and agency mechanisms, rather than spatial realignment alone. The video self-observation and error-based learning paradigms show particular promise. Future research should focus on controlled trials, longitudinal tracking, and the integration of individualized, mechanism-specific rehabilitation models to optimize outcomes for stroke survivors with AHP. Full article

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts. Full article

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

Microbially induced carbonate precipitation (MICP), as an eco-friendly biomineralization technology, has opened up an innovative path for the green and low-carbon development of the mining industry. Unlike conventional methods, its in situ solidification minimizes environmental disturbances and reduces carbon emissions during construction. This article reviews the research on MICP technology in various scenarios within the mining industry, summarizes the key factors influencing the application of MICP, and proposes a future research direction to fill the gap of the lack of systematic guidance for the application of MICP in this field. Specifically, it elaborates on the solidification mechanism of MICP and its current application in the solidification and storage of tailings, heavy metal immobilization, waste resource utilization, carbon sequestration, and field-scale deployment, establishing a technical foundation for broader implementation in the mining sector. Key influencing factors that affect the solidification effect of MICP are discussed, along with critical engineering challenges such as the attenuation of microbial activity and the low uniformity of calcium carbonate precipitation under extreme conditions. Proposed solutions include environmentally responsive self-healing technologies (the stimulus-responsive properties of the carriers extend the survival window of microorganisms), a one-phase low-pH injection method (when the pH = 5, the delay time for CaCO₃ to appear is 1.5 h), and the incorporation of auxiliary additives (the auxiliary additives provided more adsorption sites for microorganisms). Future research should focus on in situ real-time monitoring of systems integrated with deep learning, systematic mineralization evaluation standard system, and urea-free mineralization pathways under special conditions. Through interdisciplinary collaboration, MICP offers significant potential for integrated scientific and engineering solutions in mine waste solidification and sustainable resource utilization. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article

Air pollution is causing a global health, climate, and environmental crisis. Air quality (AQ) in hyper-arid regions, such as the Arabian Peninsula, remains under-explored, posing significant concerns for public health and the scientific community. Both long-term and short-term exposure to high pollutant levels, whether from anthropogenic or natural sources, can pose serious health risks. This paper offers a comprehensive review and meta-analysis of urban AQ literature published in the region over the past decade (2013–June 2025). We aim to provide guidance and highlight key directions for future research in the field. This paper examines key pollutants, emission sources, implications of urban sources, and the most studied countries, methodologies, limitations, and recommendations from different case studies. Our analysis reveals a significant research gap highlighting insufficient recent literature. Saudi Arabia was the most studied country with 20 papers, followed by the broader Arabian Peninsula (sixteen), Qatar (twelve), the United Arab Emirates and Iraq (seven each), Kuwait (four), Oman (three), Jordan, and Bahrain (one each). The primary methods employed included measurements and sampling (28%) and remote sensing (24%), with a focus on pollutants such as dust (23.1%), NO_x/NO₂/NO (17.2%), PM_2.5 (17.6%), and PM₁₀ (12%). Industrial emissions (27%) and natural dust (24%) were identified as significant emission sources. Monitoring methods included grab sampling (19%), integrated sampling (34%), and continuous monitoring (47%). Notably, 13.3% of AQ sensors were linked to a station, 27.6% were self-referenced, and 59.1% did not specify calibration methods. The findings highlight the need for further research, regular calibration of air quality monitors, and the integration of advanced modeling approaches. Moreover, we recommend exploring the links between air pollution and urban development to ensure cleaner air and contribute to the global dialogue on sustainable and cross-border AQ solutions. Full article

This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article

Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies—such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets—can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., Akkermansia muciniphila abundance, SCFA production). In parallel, digital health tools—including mobile health applications, wearable devices, and AI-supported platforms—enhance self-monitoring, adherence, and dynamic feedback in real-world settings. Mechanistic pathways such as gut–brain axis regulation, microbial fermentation, gene–diet interactions, and anti-inflammatory responses are explored to explain inter-individual differences in dietary outcomes. However, challenges such as cost, accessibility, and patient motivation remain and should be addressed to ensure the effective implementation of these integrated strategies in real-world settings. Collectively, these insights underscore the pivotal role of precision nutrition as a cornerstone for personalized, scalable, and sustainable obesity interventions. Full article

Background: Adolescence is a critical period of physical, psychological, and social development, during which athletes are particularly vulnerable to stress and injuries. Competitive anxiety has been identified as a psychological factor that may increase injury risk; however, its role among young soccer players remains underexplored. Objectives: This study aimed to analyse the association between competitive anxiety and injury vulnerability in young male soccer players aged 10 to 15 years. Methods: A total of 322 male soccer players from youth categories (Alevin, Infantil, and Cadete) participated. Competitive anxiety was assessed using the Sport Anxiety Scale-2 (SAS-2), and injury data were collected via a self-reported questionnaire covering the 2024–2025 season. Descriptive, comparative, and correlational analyses were conducted using non-parametric tests. Results: A high incidence of injuries was observed, increasing progressively with age category. In the overall sample, injuries were associated with higher levels of Somatic Anxiety, as well as with age and sporting experience, variables also linked to increased Worry and reduced Distraction. When analysed by category, no significant associations between anxiety and injury were found in Alevin players. In the Infantil group, injury incidence showed a slight increase with age and experience, but no association with anxiety was detected. Among Cadete players, injuries were positively related to Somatic Anxiety and Distraction, highlighting the influence of psychological factors at this developmental stage. Conclusions: These findings underscore the relevance of competitive anxiety, particularly Worry and Distraction, as risk factors for injury in youth soccer. The implementation of preventive psychological interventions and ongoing monitoring is recommended to reduce anxiety levels and injury vulnerability, thereby promoting safer and healthier athletic development among young soccer players. Full article

Background/Objectives: Patients with inappropriate sinus tachycardia (IST) and postural orthostatic tachycardia syndrome (POTS) exhibit complex clinical profiles due to autonomic dysfunction. While sinus node sparing (SNS) hybrid ablation is emerging as a promising therapy, there are no established guidelines worldwide for post-procedure patient management and care is mainly based on telemonitoring. In contrast, our hybrid cardiac rehabilitation (HCR) program integrates inpatient care and home-based telerehabilitation. We aim to evaluate the implementation of the HCR program, patient acceptance and adherence, and the effectiveness of the Malmö POTS scoring system in monitoring disease progression and rehabilitation outcomes. Methods: Patients underwent a personalized HCR program after SNS. The program included early mobilization, psychological support, respiratory therapy, and structured exercise. Clinical outcomes were assessed using symptom burden (Malmö POTS score), ECG parameters, exercise duration, perceived exertion, and rehabilitation adherence. Results: All patients completed the inpatient phase, and 87% completed the home-based phase. In the early postoperative period, pericarditis, anemia, and benign rhythm disturbances were mild and self-limiting. The Malmö POTS score decreased from 65.3 to 25.7. Lower perceived exertion early in the program correlated with clinical improvement. At the 2-month follow-up, 81% of patients no longer met the clinical criteria for IST/POTS without the use of medications. The program was evaluated as safe, feasible, and well-tolerated, with high patient satisfaction. Conclusions: A well-organized hybrid cardiac rehabilitation program after SNS is feasible, safe, and well-tolerated in IST/POTS patients. The Malmö POTS score may support outcome monitoring. The integration of individualized training and telemedicine represents a promising development for patients post-SNS ablation. While this study demonstrates feasibility and potential benefits, further controlled studies are needed to evaluate its impact on long-term recovery and symptom control. Full article

GNSS-Inertial integration brings great potential to detect and mitigate the effect of erroneous (spoofed) GNSS data. When a trajectory of an airplane diverges from (or is inconsistent with) inertial data, the integrated system may detect this erroneous GNSS trajectory and may be able to maintain navigation integrity by rejecting this data. A GNSS-Aided Inertial System can provide both self-contained detection of a GNSS spoofing event as well as mitigation, where mitigation is hard to achieve globally with other commercial aviation systems relying on good ground system coverage. This paper provides an overview of the newly developed Inertial Spoofing Monitor for aviation grade navigation systems, which was designed to detect multiple simultaneous erroneous (spoofed) satellite measurements. The Inertial Spoofing Monitor was then thoroughly tested, and simulations were performed to evaluate and demonstrate the detection, mitigation, and recovery capability of the spoofing monitor. The performance validation followed the process prescribed by Appendix Q of the RTCA DO-384 MOPS (Minimum Operation Performance Standard). The results show great detection, mitigation, and recovery performance of the developed Inertial Spoofing Monitor, but also indicate constraints regarding the assumed sensor error model. Full article